Object detection device

ABSTRACT

Provided is an object detection device capable of improving efficiency while ensuring detection accuracy.An object detection device 1 includes a point cloud acquisition unit 201 that acquires point cloud data of an object according to a scanning result of a LiDAR 101, an object detection unit 204 that detects an object based on the point cloud data, and a reliability determination unit 205 that determines a reliability in a detection result of the object detection unit 204. The point cloud acquisition unit 201 controls the scanning range and the irradiation density of the LiDAR 101 on the basis of the reliability.

TECHNICAL FIELD

The present invention relates to an object detection device.

BACKGROUND ART

In automatic driving, highly accurate detection of an object is requiredin order to avoid collision with another vehicle or an obstacle presentaround the vehicle. As an object detection technique, an objectdetection technique using light detection and ranging (LiDAR), radar, orthe like that emits a laser beam to scan the periphery of a vehicle isknown.

In general, in an object detection technique using LiDAR, an irradiationpoint of a laser beam with respect to a distant object or a small objectbecomes sparse, so that it is difficult to detect a distant object or asmall object. In recent years, LiDAR models capable of dynamicallychanging a scanning range and an irradiation density have appeared. TheLiDAR capable of dynamically changing the scanning range and theirradiation density can clearly detect the type of object by scanning adistant object or a small object with an increased irradiation density.However, since it is inefficient to increase the irradiation density forscanning all objects, it is important to select an object to be scannedby increasing the irradiation density.

PTL 1 describes a monitoring device that detects an object using a laserbeam emitted from a light source and reflected by the object. In a casewhere a plurality of objects is detected, the monitoring devicedescribed in PTL 1 scans the irradiation point where the object havingthe highest relative speed with respect to the own vehicle among theplurality of objects is detected in a mode in which the irradiationdensity is increased in preference to other irradiation points.

CITATION LIST Patent Literature

-   PTL 1: JP 5978565 B2

SUMMARY OF INVENTION Technical Problem

However, in the monitoring device described in PTL 1, the mode forincreasing the irradiation density of the laser beam is canceled onlywhen an object that is a target of the mode is no longer detected. Themonitoring device described in PTL 1 scans an object in the mode as longas the object is detected regardless of the position of the object inthe mode. Therefore, the monitoring device described in PTL 1 continuesscanning in the mode even after the scanning in the mode becomesunnecessary, which is inefficient. In addition, in the monitoring devicedescribed in PTL 1, even if an unknown object appears during scanning inthis mode, there is a possibility that the unknown object cannot bedetected.

The present invention has been made in view of the above, and an objectof the present invention is to provide an object detection devicecapable of improving efficiency while securing detection accuracy.

Solution to Problem

In order to solve the above problem, an object detection deviceaccording to the present invention includes: a point cloud acquisitionunit configured to acquire point cloud data of an object existing in aperiphery of a vehicle according to a scanning result of a sensor thatscans the periphery of the vehicle; an object detection unit configuredto detect the object based on the point cloud data; and a reliabilitydetermination unit configured to determine a reliability in a detectionresult of the object detection unit. The point cloud acquisition unitcontrols a scanning range and an irradiation density of the sensor basedon the reliability.

Advantageous Effects of Invention

According to the present invention, it is possible to provide an objectdetection device capable of improving efficiency while securingdetection accuracy. Objects, configurations, and effects besides theabove description will be apparent through the explanation on thefollowing embodiments.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating a hardware configuration of an objectdetection device according to Embodiment 1.

FIG. 2 is a diagram illustrating a functional configuration of theobject detection device illustrated in FIG. 1 .

FIG. 3 is a diagram illustrating a coordinate system used in the objectdetection device illustrated in FIG. 2 .

FIG. 4 is a diagram for explaining a data structure of data stored in apoint cloud DB illustrated in FIG. 2 .

FIG. 5 is a state transition diagram related to a scanning mode of LiDARillustrated in FIG. 2 .

FIG. 6 is a diagram for explaining a data structure of a caution-neededobject list illustrated in FIG. 5 .

FIG. 7 is a flowchart illustrating a flow of processing performed by theobject detection device illustrated in FIG. 2 .

FIG. 8 is a diagram for explaining a caution-needed area illustrated inFIG. 7 .

FIG. 9(a) is a diagram for explaining a case where a preceding vehiclebecomes a scanning target in a narrowing mode from the situationillustrated in FIG. 8 , and FIG. 9(b) is a diagram for explaining a casewhere another preceding vehicle becomes a scanning target in thenarrowing mode from the situation illustrated in FIG. 8 .

FIG. 10 is a flowchart illustrating a flow of object detectionprocessing shown in Step S703 and Step S710 of FIG. 7 .

FIG. 11 is a diagram for explaining an occupancy grid map used for theobject detection processing illustrated in FIG. 10 .

FIG. 12 is a diagram for explaining a data structure of a group createdin the object detection processing illustrated in FIG. 10 .

FIG. 13 is a flowchart illustrating a flow of processing related to roadsurface estimation and point cloud data classification illustrated inStep S1002 of FIG. 10 .

FIG. 14 is a flowchart illustrating a flow of processing related togrouping illustrated in Step S1003 of FIG. 10 .

FIG. 15(a) is a diagram for explaining the processing of FIG. 14 , andis a diagram illustrating a grouping situation in a case where theobject detection device sets a search area for the first time, and FIG.15(b) is a diagram illustrating a grouping situation in a case where theprocessing proceeds from FIG. 15(a).

FIG. 16 is a flowchart illustrating a flow of processing related tospecification of a parameter of a group illustrated in Step S1004 ofFIG. 10 .

FIG. 17 is a flowchart illustrating a flow of processing related tocalculation of reliability illustrated in Step S1005 of FIG. 10 .

FIG. 18 is a diagram for explaining a score calculation methodillustrated in Step S1701 of FIG. 17 .

FIG. 19 is a diagram for explaining a calculation example of a score anda reliability using FIG. 18 .

FIG. 20 is a diagram for explaining another calculation example of ascore and a reliability different from those in FIG. 19 .

FIG. 21(a) is a diagram for explaining a lower limit value in a range ofan estimated value of a parameter of a detection object in a case whereone object is scanned in a normal mode, FIG. 21(b) is a diagram forexplaining an upper limit value in a range of an estimated value of aparameter of a detection object in the case illustrated in FIG. 21(a),and FIG. 21(c) is a diagram illustrating a case where two objects arescanned in the normal mode and the object detection device detects thetwo objects as one object.

FIG. 22 is a diagram for explaining a modification of the scorecalculation method.

FIG. 23(a) is a diagram for explaining a lower limit value in the rangeof the estimated value of the parameter of the detection object in acase where the object illustrated in FIG. 21(a) is re-scanned in thenarrowing mode, FIG. 23(b) is a diagram for explaining an upper limitvalue in the range of the estimated value of the parameter of thedetection object in a case illustrated in FIG. 23(a), and FIG. 23(c) isa diagram illustrating a case where the object illustrated in FIG. 21(c)is re-scanned in the narrowing mode and the object detection devicedetects the object as a separate object.

FIG. 24 is a diagram illustrating an example of a detection object in anobject detection device according to Embodiment 2.

FIG. 25 is a flowchart illustrating a flow of processing related tocalculation of a reliability performed by the object detection device ofEmbodiment 2.

FIG. 26 is a flowchart illustrating a flow of processing performed by anobject detection device of Embodiment 3.

DESCRIPTION OF EMBODIMENTS

Hereinafter, embodiments of the present invention will be describedusing the drawings. Configurations denoted by the same referencenumerals in the respective embodiments have similar functions in therespective embodiments unless otherwise specified, and thus thedescription thereof will be omitted.

Embodiment 1

FIG. 1 is a diagram illustrating a hardware configuration of an objectdetection device 1 according to Embodiment 1.

The object detection device 1 is a device that is mounted on a vehicleand monitors the surroundings of the vehicle. The object detectiondevice 1 is a device that detects an object existing in the periphery ofa vehicle. The object detection device 1 is connected to a vehiclecontrol unit 102 via a CAN bus 107.

The object detection device 1 includes a LiDAR 101, a storage device103, a CPU 104, a memory 105, and a CAN I/F 106.

The LiDAR 101 emits one or a plurality of laser beams from the vehicletoward the surroundings, receives reflected light thereof, and acquiresdata such as a distance, a direction, and a reflection intensity of thelaser beam to a reflection position. This data is converted into pointcloud data. Unlike using a camera, the object detection device 1 usingthe LiDAR 101 can acquire point cloud data without depending on thebrightness of the surroundings, and can detect an object regardless ofday or night or the weather. The LiDAR 101 is connected to the CPU 104via an Ethernet 108 and transmits acquired data to the CPU 104. Aprotocol in this connection generally uses a user datagram protocol(UDP).

The storage device 103 stores map information and data necessary forautomatic driving. The storage device 103 can also store a value of acalibration parameter of the LiDAR 101.

The CPU 104 converts the data acquired by the LiDAR 101 into point clouddata, and performs object detection processing of detecting an objectexisting in the periphery of the vehicle on the basis of the point clouddata. The CPU 104 causes the memory 105 to store data and processingresults required in the processing. For example, the CPU 104 transmitsthe point cloud data converted from the data acquired by the LiDAR 101to the memory 105, and stores the point cloud data in, for example, abuffer corresponding to the data structure illustrated in FIG. 4 . TheCPU 104 controls the CAN I/F 106 such that a processing result istransmitted to the vehicle control unit 102 via the CAN bus 107. The CPU104 acquires dead reckoning information such as a traveling directionand a speed of the vehicle created by the vehicle control unit 102 viathe CAN I/F 106.

The vehicle control unit 102 includes an ECU or the like that controlsthe traveling direction and speed of the vehicle. The vehicle controlunit 102 takes in a processing result of the object detection processingby the CPU 104 and controls traveling of the vehicle. The vehiclecontrol unit 102 creates the dead reckoning information of the travelingdirection and the speed of the vehicle, and transmits the dead reckoninginformation to the CPU 104 via the CAN bus 107 and the CAN I/F 106.

FIG. 2 is a diagram illustrating a functional configuration of objectdetection device 1 illustrated in FIG. 1 .

The object detection device 1 includes a point cloud acquisition unit201, an object detection unit 204, a reliability determination unit 205,and a point cloud DB 208.

The point cloud acquisition unit 201 acquires point cloud data of anobject existing in the periphery of the vehicle according to a scanningresult of the LiDAR 101, which is a sensor that scans around thevehicle. The point cloud acquisition unit 201 includes a point cloudacquisition control unit 202 that controls the scanning range and theirradiation density of the LiDAR 101 on the basis of the determinationresult of the reliability determination unit 205, and a point cloudposition calculation unit 203 that acquires point cloud data from thescanning result of the LiDAR 101. The irradiation density is the numberof irradiation points per unit volume of an object irradiated with laserbeams emitted from the LiDAR 101. As the irradiation density increases,the point cloud density, which is the number of individualthree-dimensional points included in the point cloud data per unitvolume, also increases. As the irradiation density increases, theinterval between the irradiation points decreases, and the point cloudinterval, which is the interval between the three-dimensional points,also decreases. The point cloud position calculation unit 203 acquirespoint cloud data by converting data such as the distance and directionto the reflection position of the laser beam acquired by the LiDAR 101into three-dimensional points having three-dimensional coordinates andthe like. The point cloud position calculation unit 203 transmits theacquired point cloud data to the point cloud DB 208 and the objectdetection unit 204.

The point cloud DB 208 stores the point cloud data transmitted from thepoint cloud position calculation unit 203 of the point cloud acquisitionunit 201. The object detection unit 204 detects surrounding objects onthe basis of the point cloud data acquired by the point cloudacquisition unit 201. Specifically, the object detection unit 204detects the position and type of the object according to thethree-dimensional coordinates included in the point cloud datatransmitted from the point cloud position calculation unit 203 of thepoint cloud acquisition unit 201.

The reliability determination unit 205 determines the reliability in thedetection result of the object detection unit 204. Specifically, thereliability determination unit 205 calculates the reliability of theobject (hereinafter, also referred to as a “detection object”) to bedetected by the object detection unit 204 for each predetermined type ofobject, and determines the type of the detection object according to thecalculated reliability. The reliability is an index obtained byquantifying whether the type of the object can be determined for eachdetection object. The numerical range of the reliability is more than0.0 and 1.0 or less. As the reliability is higher, the type of theobject is more easily determined.

The reliability determination unit 205 includes a score calculation unit206 that calculates the reliability using the score of each parameter ofthe detection object and determines the type of the detection objectaccording to the calculated reliability, and an object tracking unit 207that tracks the movement of the detection object. The parameter is anindex representing an attribute of an object of interest when the typeof the detection object is determined. The parameter is, for example, asize of the object such as a depth, a width, and a height of the object,a speed of the object, or the like. The parameter has different possibleranges for each type of object. In the score calculation unit 206, aparameter and a possible range of the parameter for each type aredetermined in advance. In the present embodiment, each of the depth, thewidth, and the height of the object that determines the size of theobject is used as a parameter. The score is an index obtained byquantifying how much the parameter of the detection object conforms tothe possible range of the parameter for each type. The score calculationunit 206 calculates a score obtained by quantifying the compatibility ofthe parameter of the detection object with the range for each type. Inthe present embodiment, the score calculation unit 206 calculates ascore obtained by quantifying the compatibility of the size of thedetection object with the range for each type. That is, the scorecalculation unit 206 calculates, for each type, a score obtained byquantifying compatibility of each of the depth, the width, and theheight of the detection object with the corresponding range. Thenumerical range of the score is more than 0.0 and 1.0 or less. As thescore is higher, the parameter of the detection object easily fits therange.

The score calculation unit 206 calculates the reliability for each typeusing the calculated scores for the depth, width, and height of thedetection object. The score calculation unit 206 determines the type ofthe detection object according to the calculated reliability for eachtype. Specifically, when the reliability of a certain type is smallerthan a first reference value (for example, 0.3), the score calculationunit 206 denies the certain type as the type of the detection object.When the reliability of a certain type is greater than or equal to asecond reference value (for example, 0.6), the score calculation unit206 determines the certain type as the type of the detection object.When the reliability of a certain type is equal to or greater than thefirst reference value and smaller than the second reference value, thescore calculation unit 206 does not deny or confirm the certain type asthe type of the detection object, and determines that the type of thedetection object is unknown. The first reference value and the secondreference value are values determined in advance within a range of 0.0or more and 1.0 or less. The second reference value is larger than thefirst reference value. The score calculation unit 206 transmits thedetermination result of the type of the detection object to the pointcloud acquisition unit 201 and the object detection unit 204.

FIG. 3 is a diagram illustrating a coordinate system used in objectdetection device 1 illustrated in FIG. 2 .

In the present embodiment, an orthogonal coordinate system with theposition of a own vehicle 301 on which the object detection device 1 ismounted as an origin is used. In the present embodiment, a coordinateaxis along the traveling direction of the own vehicle 301 is an x axis,and the forward direction of the own vehicle 301 is a positive directionof the x axis. In the present embodiment, a coordinate axis along thevehicle width direction of the own vehicle 301 is a y axis, and adirection from right to left in the traveling direction of the ownvehicle 301 is a positive direction of the y axis. In the presentembodiment, a coordinate axis perpendicular to the road surface(coordinate axis along the gravity direction) is defined as the z axis,and a direction away from the road surface (anti-gravity direction) isdefined as a positive direction of the z axis.

FIG. 4 is a diagram for explaining a data structure of data stored inthe point cloud DB 208 illustrated in FIG. 2 .

The data stored in the point cloud DB 208 includes a point cloud number401 indicating the number of pieces of point cloud data 402 and eachpiece of the point cloud data 402. The data stored in the point cloud DB208 has a structure in which the point cloud data 402 is listed by thepoint cloud number 401. Each piece of the point cloud data 402 includesan x coordinate 403, a y coordinate 404, a z coordinate 405, and areflection intensity 406 of an individual three-dimensional point. The xcoordinate 403, the y coordinate 404, and the z coordinate 405 followthe coordinate system of FIG. 3 .

FIG. 5 is a state transition diagram regarding the scanning mode of theLiDAR 101 illustrated in FIG. 2 .

The LiDAR 101 can scan around the own vehicle 301 in the normal mode 501and the narrowing mode 502. The normal mode 501 is a mode in which thescanning range of the LiDAR 101 is maximized in the specification of theLiDAR 101 and the irradiation density of the LiDAR 101 is minimized inthe specification of the LiDAR 101. The narrowing mode 502 is a mode inwhich the scanning range of the LiDAR 101 is reduced more than in thenormal mode 501, and the irradiation density of the LiDAR 101 isincreased more than in the normal mode 501. The narrowing mode 502 maybe a mode in which the scanning range of the LiDAR 101 is minimized inthe specification of the LiDAR 101 and the irradiation density of theLiDAR 101 is maximized in the specification of the LiDAR 101. The objectdetection device 1 can detect a wide range of objects by scanning withthe LiDAR 101 in the normal mode 501, and can detect a specific objectin detail by scanning with the LiDAR 101 in the narrowing mode 502.

The LiDAR 101 maintains the normal mode 501 as it is when there is nocaution-needed object among the detection objects as a result ofscanning in the normal mode 501. As a result of scanning in the normalmode 501, when there is a caution-needed object in the detection object,the LiDAR 101 transitions to the narrowing mode 502. The caution-neededobject is a detection object having low reliability and determined bythe reliability determination unit 205 that the type of the object isunknown. The caution-needed object is registered in a caution-neededobject list 503 by the reliability determination unit 205. Thecaution-needed object list 503 is a list in which information ondetection objects to be scanned again in the narrowing mode 502 isstored. The caution-needed object list 503 is mounted in the memory 105and is configured to hold point cloud data constituting thecaution-needed object.

When the reliability of the caution-needed object re-scanned in thenarrowing mode 502 is high and the type of the object is determined bythe reliability determination unit 205, the LiDAR 101 transitions to thenormal mode 501. The caution-needed object of which the type isdetermined is excluded from the caution-needed object list 503 by thereliability determination unit 205. In a case where a predetermined timehas elapsed since the transition from the normal mode 501 to thenarrowing mode 502, the LiDAR 101 determines that a timeout has occurredand transitions to the normal mode 501.

That is, in the object detection device 1, the point cloud acquisitionunit 201 causes the LiDAR 101 to perform scanning in the normal mode501, and acquires the point cloud data according to the scanning result.In the object detection device 1, the object detection unit 204 detectsan object on the basis of the acquired point cloud data. In the objectdetection device 1, when the reliability in the detection result of theobject detection unit 204 is high (equal to or greater than the secondreference value), the reliability determination unit 205 determines thetype of the detection object. In the object detection device 1, when thereliability is low (equal to or greater than the first reference valueand smaller than the second reference value), the reliabilitydetermination unit 205 registers the detection object in thecaution-needed object list 503. In the object detection device 1, thepoint cloud acquisition unit 201 causes the LiDAR 101 to re-scan thedetection object registered in the caution-needed object list 503 in thenarrowing mode 502, and re-acquires the point cloud data according tothe re-scanning result. In the object detection device 1, the objectdetection unit 204 re-detects the detection object on the basis of there-acquired point cloud data. In the object detection device 1, thereliability determination unit 205 determines the reliability in there-detection result of the object detection unit 204. In the objectdetection device 1, the reliability determination unit 205 excludes thedetection object having high reliability in the re-detection result (thesecond reference value or more) from the caution-needed object list 503.Then, in the object detection device 1, the point cloud acquisition unit201 causes the LiDAR 101 to scan the new object in the normal mode 501,and acquires the point cloud data according to the scanning result.

FIG. 6 is a diagram for explaining a data structure of thecaution-needed object list 503 illustrated in FIG. 5 .

The caution-needed object list 503 includes an object number 601indicating the number of caution-needed objects and object information602 indicating detailed information on the caution-needed objects. Thecaution-needed object list 503 has a structure in which the objectinformation 602 is listed by the object number 601. The objectinformation 602 includes object coordinates 603 indicating positioncoordinates of a caution-needed object, a point cloud number 604indicating the number of pieces of point cloud data 605 constituting thecaution-needed object, and each piece of the point cloud data 605constituting the caution-needed object. The object coordinates 603include an x coordinate 606, a y coordinate 607, and a z coordinate 608.Each piece of the point cloud data 605 includes an x coordinate 609, a ycoordinate 610, a z coordinate 611, and a reflection intensity 612 of anindividual three-dimensional point.

FIG. 7 is a flowchart illustrating a flow of processing performed by theobject detection device 1 illustrated in FIG. 2 .

In Step S701, the object detection device 1 sets the scanning mode ofthe LiDAR 101 to the normal mode 501.

In Step S702, the object detection device 1 sets the caution-needed areaaround the own vehicle based on the current vehicle speed of the ownvehicle, the limit steering angle, and the predicted travelingdirection. The caution-needed area is a monitoring area that the objectdetection device 1 should be particularly careful of in the scanningrange of the LiDAR 101. The object located in the caution-needed area isan object that may be registered in the caution-needed object list as acaution-needed object. That is, the object located in the caution-neededarea is an object that may be re-scanned in the narrowing mode as thecaution-needed object. Setting of the caution-needed area will bedescribed later with reference to FIG. 8 . After setting thecaution-needed area, the object detection device 1 scans the LiDAR 101in the normal mode to acquire point cloud data of an object existing inthe periphery of the own vehicle 301.

In Step S703, the object detection device 1 performs object detectionprocessing of detecting an object on the basis of the acquired pointcloud data. As an algorithm of the object detection processing, variousalgorithms such as an occupancy grid map (OGM) can be adopted. Theobject detection device 1 of the present embodiment performs the objectdetection processing using the occupancy grid map. The object detectionprocessing will be described later with reference to FIG. 10 .

After Step S703, the object detection device 1 proceeds to Loop L71. InLoop L71, the object detection device 1 performs Steps S704 to S706 bythe number of detection objects to be detected in Step S703.

In Step S704, the object detection device 1 determines whether thedetection object is located in the caution-needed area. When thedetection object is not located in the caution-needed area, the objectdetection device 1 performs Loop L71 on another detection object. Whenthe detection object is located in the caution-needed area, the objectdetection device 1 proceeds to Step S705.

In Step S705, the object detection device 1 determines whether it isdetermined that the type of the detection object is unknown. When it isnot determined that the type of the detection object is unknown, theobject detection device 1 performs Loop L71 on another detection object.In a case where it is determined that the type of the detection objectis unknown, the object detection device 1 proceeds to Step S706.

In Step S706, the object detection device 1 registers the detectionobject of which the type is determined to be unknown in thecaution-needed object list as a caution-needed object.

After Step S706, the object detection device 1 performs Loop L71 onanother detection object. After performing Loop L71 by the number ofdetection objects, the object detection device 1 exits Loop L71 andproceeds to Step S707.

In Step S707, the object detection device 1 determines whether acaution-needed object is registered in the caution-needed object list.In a case where the caution-needed object is not registered in thecaution-needed object list, the object detection device 1 proceeds toStep S701. In a case where a caution-needed object is registered in thecaution-needed object list, the object detection device 1 proceeds toStep S708.

In Step S708, the object detection device 1 sets the scanning mode ofthe LiDAR 101 to the narrowing mode 502.

After Step S708, the object detection device 1 proceeds to Loop L72. InLoop L72, the object detection device 1 performs Steps S709 to S712 bythe number of caution-needed objects, that is, by the number ofdetection objects registered as the caution-needed objects in thecaution-needed object list.

In Step S709, the object detection device 1 sets the scanning range ofthe LiDAR 101 in accordance with the object coordinates of thecaution-needed object. The object detection device 1 causes the LiDAR101 to scan in the narrowing mode and reacquires the point cloud data ofthe caution-needed object. The setting of the scanning range of theLiDAR 101 in the narrowing mode will be described later with referenceto FIGS. 9(a) and 9(b).

In Step S710, the object detection device 1 performs object detectionprocessing on the basis of the re-acquired point cloud data. Thealgorithm of the object detection processing in Step S710 is similar tothe algorithm of the object detection processing in Step S703. Theobject detection device 1 performs processing similar to that in StepS703.

In Step S711, the object detection device 1 determines whether it isdetermined that the type of the caution-needed object is unknown. in acase where it is determined that the type of the caution-needed objectis unknown, the object detection device 1 performs Loop L72 on anothercaution-needed object. In a case where it is not determined that thetype of the caution-needed object is unknown, the object detectiondevice 1 proceeds to Step S712.

In Step S712, the object detection device 1 excludes the caution-neededobject whose type is not determined to be unknown from thecaution-needed object list.

After Step S712, the object detection device 1 performs Loop L72 onanother caution-needed object. The object detection device 1 executesLoop L72 as many as the number of caution-needed objects, exits LoopL72, and proceeds to Step S713.

In Step S713, the object detection device 1 determines whether a timeoutoccurs. In a case where a predetermined time has elapsed after thenarrowing mode is set in Step S708, the object detection device 1determines that a timeout has occurred. In a case where the timeout hasnot occurred, the object detection device 1 proceeds to Step S707. In acase where the timeout has occurred, the object detection device 1proceeds to Step S701.

FIG. 8 is a diagram for explaining a caution-needed area 801 illustratedin FIG. 7 . FIG. 9(a) is a diagram for explaining a case where apreceding vehicle 802 becomes a scanning target in the narrowing modefrom the situation illustrated in FIG. 8 . FIG. 9(b) is a diagram forexplaining a case where another preceding vehicle 803 becomes a scanningtarget in the narrowing mode from the situation illustrated in FIG. 8 .

The caution-needed area 801 is set based on the current vehicle speedand limit steering angle of the own vehicle 301, and a predictedtraveling direction 302. The limit steering angle is a maximum steeringangle at which the vehicle does not fall. The dynamic theoretical valueof the limit steering angle is described in, for example, Wada et al.,“Study on steering control by joystick-type automobile driving device”(Journal of the Society of Instrument and Control Engineers Vol. 49, No.4, 417/424, 2013). This document describes the finding that the limitsteering angle decreases as the vehicle speed increases. The limitsteering angle changes as indicated by 805. The object detection device1 stores a table or the like indicating the relationship between thevehicle speed of the own vehicle 301 and the limit steering angle in thestorage device 103 or the memory 105 in advance.

The object detection device 1 specifies the limit steering anglecorresponding to the current vehicle speed of the own vehicle 301 byreferring to a table indicating the relationship between the vehiclespeed and the limit steering angle. Here, the limit steering angle is θ,and the longest distance that the LiDAR 101 can measure is d. The objectdetection device 1 sets, as the caution-needed area 801, a fan-shapedarea centered on the own vehicle 301, having a radius of d and a centralangle of 2θ, and extending forward in the traveling direction that isthe predicted traveling direction 302 of the own vehicle 301.

In the example of FIG. 8 , the preceding vehicle 802 and the precedingvehicle 803 of the own vehicle 301 are located inside the caution-neededarea 801, but a preceding vehicle 804 is located outside thecaution-needed area 801. When it is determined that the types of thepreceding vehicle 802 and the preceding vehicle 803 are unknown, theobject detection device 1 registers the preceding vehicle 802 and thepreceding vehicle 803 as caution-needed objects in the caution-neededobject list, and performs re-scanning in the narrowing mode. Even whenit is determined that the type of the preceding vehicle 804 is unknown,the object detection device 1 does not register the preceding vehicle804 in the caution-needed object list and does not perform re-scanningin the narrowing mode. That is, the preceding vehicle 802 and thepreceding vehicle 803 located inside the caution-needed area 801 can bescanning targets in the narrowing mode, but the preceding vehicle 804located outside the caution-needed area 801 is not a scanning target inthe narrowing mode.

FIG. 9(a) illustrates a case where the preceding vehicle 802 becomes ascanning target in the narrowing mode. In this case, the objectdetection device 1 sets a scanning range 901 of the LiDAR 101 in thenarrowing mode in accordance with the object coordinates of thepreceding vehicle 802. The object detection device 1 causes the LiDAR101 to scan in the set scanning range 901 to acquire the point clouddata of the preceding vehicle 802. FIG. 9(b) illustrates a case wherethe preceding vehicle 803 becomes a scanning target in the narrowingmode. In this case, the object detection device 1 sets a scanning range902 of the LiDAR 101 in the narrowing mode in accordance with the objectcoordinates of the preceding vehicle 803. The object detection device 1causes the LiDAR 101 to scan in the set scanning range 902 to acquirethe point cloud data of the preceding vehicle 803.

FIG. 10 is a flowchart illustrating a flow of the object detectionprocessing illustrated in Step S703 and Step S710 of FIG. 7 .

In Step S1001, the object detection device 1 stores the all point clouddata in each two-dimensional grid constituting the occupancy grid map.In the object detection device 1, in order to implement the objectdetection function by a processing flow that does not depend on the typeof the LiDAR 101, a coordinate system that does not depend on the typeof the LiDAR 101 is required. Therefore, it is effective to use theoccupancy grid map. The size of the grid can be arbitrarily determinedaccording to the size of the object to be detected.

In Step S1002, the object detection device 1 classifies the point clouddata into the road surface and the object. Specifically, the objectdetection device 1 estimates the road surface in the occupancy grid map,and determines the point cloud data constituting the estimated roadsurface. The object detection device 1 determines the point cloud dataconstituting the object from the point cloud data other than the pointcloud data constituting the road surface. As a result, the objectdetection device 1 can classify the point cloud data into the roadsurface and the object. The object detection device 1 determines thatthe point cloud data of which of the road surface and the object isunknown is unknown. Details of the processing related to the roadsurface estimation and the classification of the point cloud data willbe described later with reference to FIG. 13 .

In Step S1003, the object detection device 1 groups the point cloud dataclassified as the object. Specifically, in the point cloud dataclassified as the object in Step S1002, the object detection device 1groups the adjacent point cloud data whose positions are arranged withina predetermined distance to create a group. The group represents adetection object. Details of processing related to grouping will bedescribed later with reference to FIG. 14 .

After Step S1003, the object detection device 1 proceeds to Loop L101.In Loop L101, the object detection device 1 performs Steps S1004 toS1006 by the number of groups created by grouping in Step S1003.

In Step S1004, the object detection device 1 specifies parameters of thegroup. The parameters of the group are the depth, width and height ofthe group. The parameters of the group may include position coordinatesof the group. The parameters of the group represent parameters of thedetection object. Details of the processing related to the specificationof the parameters of the group will be described later with reference toFIG. 16 .

In Step S1005, the object detection device 1 calculates the reliabilityfor each type of detection object on the basis of the parameter of thegroup. Processing related to calculation of the reliability will bedescribed later with reference to FIG. 17 .

In Step S1006, the object detection device 1 determines the type of thedetection object on the basis of the calculated reliability for eachtype. As described above, when the reliability of a certain type issmaller than the first reference value (for example, 0.3), the objectdetection device 1 denies the certain type as the type of the detectionobject. When the reliability of a certain type is greater than or equalto a second reference value (for example, 0.6), the score calculationunit 206 determines the certain type as the type of the detectionobject. When the reliability of a certain type is equal to or greaterthan the first reference value and smaller than the second referencevalue, the score calculation unit 206 does not deny or confirm thecertain type as the type of the detection object, and determines thatthe type of the detection object is unknown.

After Step S1006, the object detection device 1 performs Loop L101 foranother group. After performing Loop L101 by the number of groups, theobject detection device 1 exits Loop L101 and ends the processingillustrated in FIG. 10 .

FIG. 11 is a diagram for explaining an occupancy grid map used for theobject detection processing illustrated in FIG. 10 .

Similarly to FIG. 3 , an occupancy grid map 1101 has a coordinate systemin which a direction along the traveling direction of the own vehicle isan X index and a direction along the vehicle width direction of the ownvehicle is a Y index. In the occupancy grid map 1101, each grid has apredetermined same size. Each grid holds data including point cloud data1104. As illustrated in FIG. 11 , the data held by each of the gridsincludes a group ID 1102 which is identification information of thegroup, a point cloud number 1103 indicating the number of point clouddata 1104 constituting the group, and each of the point cloud data 1104constituting the group. Each of the point cloud data 1104 includes an xcoordinate 1105, a y coordinate 1106, a z coordinate 1107, a reflectionintensity 1108, and a classification 1109 for an individualthree-dimensional point. The classification 1109 indicates whether thepoint cloud data is classified as an object, a road surface, or theclassification is unknown.

FIG. 12 is a diagram for explaining a data structure of a group createdin the object detection processing illustrated in FIG. 10 .

The data of the group created in Step S1003 of FIG. 10 includes thegroup number 1201 indicating the number of pieces of group data 1202 andeach piece of group data 1202. Each piece of group data 1202 includes agroup ID 1203, a depth 1204, a width 1205, and a height 1206 which areparameters of the group, a constituent grid number 1207 indicating thenumber of grids constituting the group, and grid coordinates 1208indicating coordinates of individual grid constituting the group. Thegrid coordinates 1208 include an X index 1209 and a Y index 1210.

FIG. 13 is a flowchart illustrating a flow of processing related to roadsurface estimation and point cloud data classification illustrated inStep S1002 of FIG. 10 .

In Step S1301, the object detection device 1 divides the occupancy gridmap into a plurality of regions for each predetermined region. Since theroad surface indicated by the entire occupancy grid map is notnecessarily constituted by one plane, it is appropriate to express theroad surface by a combination of several planes. The object detectiondevice 1 divides the occupancy grid map into a plurality of regions, andestimates the road surface by a combination of local features. In thepresent embodiment, each of a plurality of regions obtained by dividingthe occupancy grid map for each predetermined region is also referred toas a “divided region”.

After Step S1301, the object detection device 1 proceeds to Loop L131.In Loop L131, the object detection device 1 performs Steps S1302 andS1303, and Loop L132 by the number of divided regions indicating thenumber of the plurality of regions divided in Step S1301.

In Step S1302, the object detection device 1 extracts point cloud dataas a road surface candidate in the divided region to be processed. Forexample, in a case where the z coordinate of the point cloud data heldin each grid in the divided region to be processed is within a range of±10 cm of the road surface height of the divided region closer to theown vehicle than the divided region, the object detection device 1extracts the point cloud data as the point cloud data as the roadsurface candidate. However, when the divided region to be processed isthe divided region closest to the own vehicle, the object detectiondevice 1 extracts the point cloud data as the point cloud data as theroad surface candidate when the z coordinate of the point cloud dataheld in each grid in the divided region to be processed is within therange of ±10 cm of the z coordinate (the z coordinate of the origin) ofthe own vehicle.

In Step S1303, the object detection device 1 applies random sampleconsensus (RANSAC), which is one of robust estimation, to the extractedpoint cloud data to calculate the road surface parameter. For example,when the road surface is regarded as a plane, the z coordinate of thepoint cloud data can be represented by the following Expression (1).

z=ax+by+c  (1)

The object detection device 1 calculates the values of the coefficientsa, b, and c in Expression (1) using RANSAC. The road surface parameteris coefficients a, b, and c of Expression (1). The object detectiondevice 1 may apply a least squares method instead of RANSAC to theextracted point cloud data, or may apply a combination of RANSAC and aleast squares method.

After Step S1303, the object detection device 1 proceeds to Loop L132 toclassify all the point cloud data in the divided region to be processedinto the road surface and the object. In Loop L132, the object detectiondevice 1 performs Loop L133 by the number of grids in the divided regionto be processed. In Loop L133, the object detection device 1 performsSteps S1304 to S1309 by the number of point groups that is the number ofpoint cloud data held in each grid.

In Step S1304, the object detection device 1 acquires the z coordinate(for example, the z coordinate 1107 in FIG. 11 ) of the point cloud dataheld in the grid in the divided region to be processed.

In Step S1305, the object detection device 1 calculates the road surfaceheight indicated by the x coordinate and the y coordinate of the pointcloud data of which the z coordinate is acquired in Step S1304.Specifically, the object detection device 1 calculates the road surfaceheight from the coefficients a, b, and c (road surface parameters)calculated in Step S1303 by using the following Expression (2).

Road surface height=a×(x coordinate)+b×(y coordinate)+c  (2)

In Step S1306, the object detection device 1 compares the z coordinateacquired in Step S1304 with the road surface height calculated in StepS1305. In a case where the acquired z coordinate is equal to or higherthan the road surface height and is in a range smaller than the roadsurface height+5 cm, the object detection device 1 proceeds to StepS1307. When the acquired z coordinate is greater than or equal to theroad surface height+5 cm, the object detection device 1 proceeds to StepS1308. When the acquired z coordinate is smaller than the road surfaceheight, the object detection device 1 proceeds to Step S1309.

In Step S1307, the object detection device 1 determines that theclassification of the point cloud data of which the z coordinate isacquired in Step S1304 is the road surface.

In Step S1308, the object detection device 1 determines that theclassification of the point cloud data of which the z coordinate isacquired in Step S1304 is the object.

In Step S1309, the object detection device 1 determines that theclassification of the point cloud data of which the z coordinate isacquired in Step S1304 is unknown.

After Step S1307, Step S1308, or Step S1309, the object detection device1 performs Loop L133 on another piece of point cloud data held in thegrid in the divided region to be processed. After performing Loop L133by the number of pieces of point cloud data held in the grid, the objectdetection device 1 exits Loop L133 and performs Loop L132 on anothergrid in the divided region. After performing Loop L132 by the number ofgrids in the divided region, the object detection device 1 exits LoopL132 and performs Loop L131 on another divided region. After performingLoop L131 by the number of divided regions, the object detection device1 exits Loop L131 and ends the processing illustrated in FIG. 13 .

FIG. 14 is a flowchart illustrating a flow of processing related togrouping illustrated in Step S1003 of FIG. 10 .

The object detection device 1 performs Loop L141 for performing StepsS1401 to S1408 by the number of grids of the occupancy grid map.

In Step S1401, the object detection device 1 determines whether thepoint cloud data is held in the grid to be processed. In a case wherethe point cloud data is not held in the grid, the object detectiondevice 1 performs Loop L141 on another grid. In a case where the pointcloud data is held in the grid, the object detection device 1 proceedsto Step S1402.

In Step S1402, the object detection device 1 sets a search area around agrid to be processed. The search area is an area set in the occupancygrid map for searching grids constituting the same group. The size ofthe search area is a predetermined size.

After Step S1402, the object detection device 1 proceeds to Loop L142 inorder to extract the point cloud data classified as the object in thesearch area and put together as a group. In Loop L142, the objectdetection device 1 performs Steps S1403 and S1404 by the number of gridsin the set search area.

In Step S1403, the object detection device 1 determines whether theclassification of the point cloud data held in the grid in the setsearch area is an object. When the classification of the point clouddata is not an object, the object detection device 1 performs Loop L142on another grid in the search area. In a case where the classificationof the point cloud data is an object, the object detection device 1proceeds to Step S1404.

In Step S1404, the object detection device 1 adds a grid holding thepoint cloud data determined to be classified as an object in Step S1403to the group to which the set search area belongs.

After Step S1404, the object detection device 1 performs Loop L142 onanother grid in the set search area. After performing Loop L142 by thenumber of grids in the search area, the object detection device 1 exitsLoop L142 and proceeds to Loop L143. The object detection device 1performs Loop L143 to set a group ID to a group in the set search area.In Loop L143, the object detection device 1 performs Steps S1405 andS1406 by the number of grids in the search area.

In Step S1405, the object detection device 1 determines whether a groupID is set to a grid in the set search area. When the group ID is not setto the grid, the object detection device 1 performs Loop L143 on anothergrid in the search area. In a case where the group ID is set in thegrid, the object detection device 1 proceeds to Step S1406.

In Step S1406, the object detection device 1 sets the group ID alreadyset in the grid as it is.

After Step S1406, the object detection device 1 performs Loop L143 onanother grid in the set search area. After performing Loop L143 by thenumber of grids in the search area, the object detection device 1 exitsLoop L143 and proceeds to Step S1407.

In Step S1407, the object detection device 1 determines whether thegroup ID is set to the group to which the set search area belongs. Whenthe group ID is set to the group, the object detection device 1 performsLoop L141 on another grid of the occupancy grid map. In a case where thegroup ID is not set to the group, the object detection device 1 proceedsto Step S1408.

In Step S1408, the object detection device 1 newly issues a group ID,and sets the newly issued group ID to the group to which the set searcharea belongs.

After Step S1408, the object detection device 1 performs Loop L141 onanother grid of the occupancy grid map. After performing Loop L141 bythe number of grids, the object detection device 1 exits Loop L141 andends the processing illustrated in FIG. 14 .

FIG. 15(a) is a diagram for explaining the processing of FIG. 14 , andis a diagram illustrating a grouping situation in a case where theobject detection device 1 sets a search area 1501 for the first time.FIG. 15(b) is a diagram illustrating a grouping situation in a casewhere the processing proceeds from FIG. 15(a).

In FIGS. 15 (a) and 15(b), a black or gray grid is a grid that holdspoint cloud data classified as an object. A black grid indicates a gridin which the group ID is set, and a gray grid indicates a grid in whichthe group ID is not set.

In the example of FIG. 15(a), the object detection device 1 sets thesearch area 1501 around a grid 1504. The object detection device 1 setsthe group ID as the same group for the black grid in the search area1501 including the grid 1504, a grid 1505, and a grid 1506. Thereafter,it is assumed that the object detection device 1 performs grouping bymoving the search area 1501 by one grid from the situation of FIG.15(a), and sets the search area 1501 around the grid 1506 as illustratedin FIG. 15(b).

In the example of FIG. 15(b), a group ID has already been set as thesame group for the grid 1504, the grid 1505, and the grid 1506 in thesearch area 1501. For a grid 1502 and a grid 1503 in the search area1501, the group is not determined, and the group ID is not set. In thesituation as illustrated in FIG. 15(b), the object detection device 1sets the already set group ID as it is for the grid 1504, the grid 1505,and the grid 1506 in the search area 1501. The object detection device 1adds the grid 1502 and the grid 1503 in the search area 1501 to a groupincluding the grid 1504 and the like, and the group ID set in the grid1504 and the like is set.

FIG. 16 is a flowchart illustrating a flow of processing related to thespecification of the parameters of the group illustrated in Step S1004of FIG. 10 . The processing illustrated in FIG. 16 is processingperformed to specify parameters of one group.

In Step S1601, the object detection device 1 performs main componentanalysis on the grids constituting the group. That is, the objectdetection device 1 analyzes the distribution tendency of the grid andspecifies the main component axis that can appropriately express thedirection of the group. Specifically, the object detection device 1specifies a first main component axis and a second main component axisas the main component axes, and calculates the angles of the first maincomponent axis and the second main component axis with respect to the Xindex and the Y index constituting the coordinate system of theoccupancy grid map 1101.

In Step S1602, the object detection device 1 determines whether theangle of the main component axis with respect to the X index and the Yindex has been calculated. When the object detection device 1 cannotcalculate the angle, the object detection device 1 ends the processingillustrated in FIG. 16 . When the angle can be calculated, the objectdetection device 1 proceeds to Step S1603.

In Step S1603, the object detection device 1 calculates the ratio of thelength of the second main component axis to the length of the first maincomponent axis. The first main component axis is longer than the secondmain component axis. The first main component axis and the second maincomponent axis can be considered to correspond to a long axis and ashort axis of an ellipse surrounding the grid. The object detectiondevice 1 calculates the ratio of the length of the short axis (secondmain component axis) to the length of the long axis (first maincomponent axis). The numerical range of the ratio is more than 0.0 and1.0 or less. The smaller the ratio, the higher the reliability of theangle calculated in Step S1601.

In Step S1604, the object detection device 1 compares the ratio of thelength of the second main component axis to the length of the first maincomponent axis with a predetermined threshold. The threshold is a valuethat ensures the reliability of the angle calculated in Step S1601. In acase where the ratio is larger than the threshold and equal to orsmaller than 1.0, the object detection device 1 proceeds to Step S1606.In a case where the ratio is larger than 0.0 and equal to or smallerthan the threshold, the object detection device 1 proceeds to StepS1605.

In Step S1605, the object detection device 1 fits the rectangle to thegrid at the angle calculated in Step S1601. The angle of the fittedrectangle specifies the direction of the rectangle. The length of thelong side and the length of the short side of the fitted rectanglespecify the size of the rectangle. The length of the long side and thelength of the short side of the fitted rectangle correspond to thelength of the long axis (first main component axis) and the length ofthe short axis (second main component axis). After Step S1605, theobject detection device 1 proceeds to Step S1607.

In Step S1606, the object detection device 1 fits the rectangle to thegrid while rotating the rectangle in the range of ±20 deg of the anglecalculated in Step S1601. At this time, the object detection device 1performs fitting at a rotation angle at which the number of gridsincluded in the rectangle is maximum. This method is a method calledSearch Based Rectangle Fitting (SBRF). The rectangular rotation anglerange is not necessarily limited to the range of ±20 deg of the anglecalculated in Step S1601, and can be arbitrarily determined.

In Step S1607, the object detection device 1 specifies the depth and thewidth of the group according to the direction and the size of the fittedrectangle. Specifically, the object detection device 1 specifies, as thedepth of the group, one of the long side and the short side of thefitted rectangle having a smaller angle formed with the travelingdirection of the own vehicle, and specifies the other as the width ofthe group.

In Step S1608, the object detection device 1 specifies the maximumheight of the point cloud data held by the grid in the fitted rectangle,and specifies the maximum height as the height of the group. After StepS1608, the object detection device 1 ends the processing illustrated inFIG. 16 .

FIG. 17 is a flowchart illustrating a flow of processing related to thecalculation of the reliability illustrated in Step S1005 of FIG. 10 .The processing illustrated in FIG. 17 is processing performed tocalculate reliability for each type of detection object represented byone group.

The object detection device 1 performs Loop L171 in which Steps S1701 toS1703 are performed by the number of types of objects predetermined ascalculation targets of the reliability.

In Step S1701, the object detection device 1 calculates scores of thedepth, the width, and the height which are parameters of the group. Thatis, the object detection device 1 calculates scores of the depth, thewidth, and the height which are parameters of the detection objectrepresented by the group. A score calculation method and a calculationexample will be described later with reference to FIGS. 18 to 20 .

In Step S1702, the object detection device 1 multiplies the scores ofthe depth, the width, and the height which are parameters of the group.That is, the object detection device 1 multiplies the respective scoresof the depth, the width, and the height which are parameters of thedetection object represented by the group. As a result, the product ofthe scores of the depth, the width, and the height is calculated.

In Step S1703, the object detection device 1 replaces the calculationresult of Step S1702 with the reliability. The reliability obtained inStep S1703 indicates how much the type of the detection objectrepresented by the group matches the type of the processing target. Acalculation example of the reliability will be described later withreference to FIGS. 19 and 20 .

After Step S1703, the object detection device 1 performs Loop L171 foranother type. After performing Loop L171 by the number of types, theobject detection device 1 exits Loop L171 and ends the processingillustrated in FIG. 17.

A method of calculating the score and the reliability will be describedwith reference to FIGS. 18 to 20 . FIG. 18 is a diagram for explaining ascore calculation method shown in Step S1701 of FIG. 17 . FIG. 19 is adiagram for explaining a calculation example of the score and thereliability using FIG. 18 . FIG. 20 is a diagram for explaining anothercalculation example of the score and the reliability different fromthose in FIG. 19 .

In FIG. 18 , a first threshold 1801 to a fourth threshold 1804 forcalculating the scores of the depth, the width, and the height, whichare parameters of the detection object, are described for eachpredetermined object type. FIG. 18 illustrates an example in which fourtypes of objects, that is, a pedestrian, a two-wheeled vehicle, afour-wheeled vehicle, and a truck are determined in advance as the typesof objects, but the types are not limited to these four types.

When the parameter of the detection object is smaller than the firstthreshold 1801 or larger than the fourth threshold 1804, the objectdetection device 1 sets the score of the parameter to substantially 0.0(for example, 0.01). For example, in the case that the depth of thedetection object is 0.1 m, the object detection device 1 sets the scoreto substantially 0 (for example, 0.01) because the depth is smaller than0.3 m that is the first threshold 1801. The reason why the score is setto substantially 0.0 instead of 0.0 is that the product of the scores ofthe depth, the width, and the height is set as the reliability.

When the parameter of the detection object falls within the range fromthe second threshold 1802 to the third threshold 1803, the objectdetection device 1 sets the score of the parameter to 1.0. A range fromthe second threshold 1802 to the third threshold 1803 indicates a rangethat can be taken by the parameter of the detection object.

When the parameter of the detection object is within the range from thefirst threshold 1801 to the second threshold 1802, the object detectiondevice 1 calculates the score according to the value of the parameter.Specifically, the object detection device 1 calculates the score in thiscase using the following Expression (3).

Score=(Parameter value−First threshold 1801)/(Second threshold1802−First threshold 1801)  (3)

That is, the object detection device 1 calculates the score in this caseby proportional calculation of the difference between the parametervalue and the first threshold 1801 and the difference between the secondthreshold 1802 and the first threshold 1801. The score varies between0.0 and 1.0.

Similarly, when the parameter of the detection object is within a rangelarger than the third threshold 1803 and equal to or smaller than thefourth threshold 1804, the object detection device 1 calculates thescore according to the value of the parameter. Specifically, the objectdetection device 1 calculates the score in this case using the followingExpression (4).

Score=(Parameter value−Third threshold 1803)/(Fourth threshold1804−Third threshold 1803)  (4)

That is, the object detection device 1 calculates the score in this caseby proportional calculation of the difference between the parametervalue and the third threshold 1803 and the difference between the fourththreshold 1804 and the third threshold 1803. The score varies between0.0 and 1.0.

FIG. 19 illustrates an example in which the detection object is apreceding vehicle 1901. In the example of FIG. 19 , it is assumed thatthe value of the width, which is one of the parameters of the precedingvehicle 1901 as the detection object, is 1.6 m, the value of the depth,which is one of the parameters, is 3.2 m, and the value of the height,which is one of the parameters, is 1.6 m. At this time, the objectdetection device 1 calculates scores of the depth, the width, and theheight, which are parameters of the preceding vehicle 1901, which is adetection object, for each type.

For example, when the value of the width, which is one of theparameters, is 1.6 m, since the value of the width 1.6 m is larger thanthe fourth threshold 1804 (0.6 m) for the type of pedestrian, the objectdetection device 1 sets the score of the width to 0.01. Since the widthvalue 1.6 m is larger than the fourth threshold 1804 (1.3 m) for thetype of two-wheeled vehicle, the object detection device 1 sets thewidth score to 0.01. Since the width value 1.6 m falls within the rangeof the second threshold 1802 (1.5 m) or more and the third threshold1803 (2.0 m) or less for the type of four-wheeled vehicle, the objectdetection device 1 sets the width score to 1.0. Since the width value1.6 m is within a range of the first threshold 1801 (1.4 m) or more andsmaller than the second threshold 1802 (1.7 m) for the type of track,the object detection device 1 calculates the score using the aboveExpression (3). That is, (1.6−1.4)/(1.7−1.4)=0.67 is set as a score.

The object detection device 1 calculates scores for the depth and theheight, which are other parameters, similarly to the width parameter. Asa result, a score as illustrated in FIG. 19 is obtained.

After calculating the score, the object detection device 1 calculatesthe reliability for each type by multiplying the scores of the depth,the width, and the height. For example, with respect to the type of thepedestrian, the object detection device 1 sets 0.01×0.01×1.0=0.0001 asthe reliability since the score of the width is 0.01, the score of thedepth is 0.01, and the score of the height is 1.0. The object detectiondevice 1 sets 0.01×0.01×1.0=0.0001 as the reliability for the type oftwo-wheeled vehicle. The object detection device 1 sets 1.0×1.0×1.0=1.0as the reliability for the type of four-wheeled vehicle. The objectdetection device 1 sets 0.67×1.0×0.01=0.0067 as the reliability for thetype of track.

After calculating the reliability, the object detection device 1determines the type of the detection object. In the example of FIG. 19 ,a first reference value 1902 of the reliability is 0.3, and a secondreference value 1903 is 0.6. Since the reliability (0.0001) of the typeof the pedestrian is smaller than the first reference value 1902 (0.3),the object detection device 1 determines that the pedestrian is negativeas the type of the detection object. Since the reliability (0.0001) ofthe type of the two-wheeled vehicle is smaller than the first referencevalue 1902 (0.3), the object detection device 1 determines that thetwo-wheeled vehicle is negative as the type of the detection object.Since the reliability (0.0067) of the type of the track is smaller thanthe first reference value 1902 (0.3), the object detection device 1determines that the track is negative as the type of the detectionobject. Since the reliability (1.0) of the type of the four-wheeledvehicle is greater than or equal to the second reference value 1903(0.6), the object detection device 1 determines that the four-wheeledvehicle is determined as the type of the detection object. As a result,in the example of FIG. 19 , since the object detection device 1determines that only the four-wheeled vehicle is determined as the typeof the detection object and determines that all the other types arenegative, the type of the detection object is determined as thefour-wheeled vehicle.

FIG. 20 illustrates an example in which the detection object is apreceding vehicle 2001. In a case where the score and the reliability asillustrated in FIG. 20 are calculated, the object detection device 1determines that the four-wheeled vehicle is unknown as the type of thedetection object, and determines that all the other types are negative.In this case, since the type of the detection object cannot bedetermined as a four-wheeled vehicle, the object detection device 1registers the detection object in the caution-needed object list andsets the detection object as a target of re-scanning in the narrowingmode.

As described above, the object detection device 1 of Embodiment 1includes the point cloud acquisition unit 201 that acquires the pointcloud data of the object existing in the periphery according to thescanning result of the LiDAR 101 that is a sensor that scans theperiphery of the vehicle, and the object detection unit 204 that detectsthe object based on the point cloud data. In addition, the objectdetection device 1 of Embodiment 1 includes the reliabilitydetermination unit 205 that determines the reliability in the detectionresult of the object detection unit 204. The point cloud acquisitionunit 201 controls the scanning range and the irradiation density of theLiDAR 101 on the basis of the reliability.

As a result, the object detection device 1 of Embodiment 1 canappropriately select an object to be scanned while increasing theirradiation density, so that the efficiency can be improved whileensuring the detection accuracy. The object detection device 1 ofEmbodiment 1 can efficiently suppress the risk of collision with anobject, and can contribute to safe automatic driving.

Further, in the object detection device 1 according to Embodiment 1, theLiDAR 101 can perform scanning in the normal mode and the narrowingmode. The reliability determination unit 205 determines the type of thedetection object when the reliability is high, and registers thedetection object in the caution-needed object list when the reliabilityis low. Then, the point cloud acquisition unit 201 causes the LiDAR 101to re-scan the detection objects registered in the caution-needed objectlist in the narrowing mode, and re-acquires the point cloud data. Theobject detection unit 204 re-detects the detection object on the basisof the re-acquired point cloud data. The reliability determination unit205 determines the reliability in the re-detection result of the objectdetection unit 204.

As a result, the object detection device 1 of Embodiment 1 can performscanning in the normal mode in which an object in a wide range can bedetected and the narrowing mode in which a specific object can bedetected in detail, and can switch the scanning mode according to thesituation around the vehicle. In particular, the object detection device1 of Embodiment 1 can scan only an object, such as a distant object or asmall object, for which the reliability of the detection result tends tobe low, in the narrowing mode. The object detection device 1 ofEmbodiment 1 can secure the detection accuracy even for an object whosereliability tends to be low, and does not excessively increase theirradiation density even for an object whose reliability is high.Therefore, the object detection device 1 of Embodiment 1 can furtherimprove the efficiency while securing the detection accuracy.

Further, in the object detection device 1 of Embodiment 1, when thereliability in the re-detection result is high, the reliabilitydetermination unit 205 excludes the detection object from thecaution-needed object list. The point cloud acquisition unit 201 causesthe LiDAR 101 to scan a new object different from the detection objectin the normal mode, and newly acquires point cloud data.

As a result, the object detection device 1 of Embodiment 1 can excludethe detection object having high reliability in the re-detection resultfrom the scanning target in the narrowing mode, and can quickly detecteven if an unknown object appears. Therefore, the object detectiondevice 1 of Embodiment 1 can further improve the efficiency whilesecuring the detection accuracy.

Further, in the object detection device 1 of Embodiment 1, a parameterthat is an index representing an attribute of an object and a possiblerange of the parameter for each type are determined in advance in thereliability determination unit 205. The reliability determination unit205 calculates a score that quantifies the compatibility of theparameter of the detection object with the above range, and calculatesthe reliability using the score.

As a result, the object detection device 1 of Embodiment 1 canquantitatively evaluate the reliability serving as a basis fordetermining whether to perform scanning in the narrowing mode, and thus,it is possible to further improve the efficiency while securing thedetection accuracy.

[Modification of Score Calculation Method]

A score calculation method different from that in FIGS. 18 to 20 will bedescribed with reference to FIGS. 21 to 23 .

FIG. 21(a) is a diagram for explaining a lower limit value in the rangeof the estimated value of the parameter of the detection object when oneobject is scanned in the normal mode. FIG. 21(b) is a diagram forexplaining an upper limit value in the range of the estimated value ofthe parameter of the detection object in the case illustrated in FIG.21(a). FIG. 21(c) is a diagram for explaining a case where two objectsare scanned in the normal mode and the object detection device 1 detectsthe objects as one object. FIG. 22 is a diagram for explaining amodification of the score calculation method.

FIGS. 21(a) and 21(b) illustrate a case where one object having a widthof 1.2 m, which is one of the parameters of the detection object, isscanned in the normal mode with a point cloud interval of 0.3 m. FIG.21(c) illustrates a case where two objects each having an interval of0.25 m and a total width of 1.2 m are scanned in the normal mode with apoint cloud interval of 0.3 m. In FIGS. 21(a) to 21(c), a white circleindicates the point group of the detection object, a black circleindicates the point group deviated from the detection object, and a grayportion indicates the width of the detection object estimated by theobject detection device 1.

In calculating the score, the object detection device 1 calculates arange of an estimated value which is a numerical value estimated as aparameter of the detection object from the scanning result. The objectdetection device 1 calculates the overlap ratio between the range of theestimated value of the parameter and the range in which the score of theparameter is 1.0. The object detection device 1 calculates a score basedon the overlap ratio.

The range in which the score of the parameter is 1.0 is a range from thesecond threshold 1802 to the third threshold 1803 illustrated in FIG. 18. The overlap ratio is calculated using the range of the union (OR) ofthe range of the estimated value of the parameter and the range in whichthe score of the parameter is 1.0, and the range of the intersection(AND) of the range of the estimated value of the parameter and the rangein which the score of the parameter is 1.0. Specifically, the objectdetection device 1 calculates the overlap ratio using the followingExpression (5).

Overlap ratio=(upper limit value of intersection set−lower limit valueof intersection set)/(upper limit value of sum set−lower limit value ofsum set)  (5)

That is, the object detection device 1 calculates the overlap ratio byproportional calculation of the difference between the upper limit valueand the lower limit value of the range of the union and the differencebetween the upper limit value and the lower limit value of the range ofthe intersection set. The numerical range of the overlap ratio is 0.0 ormore and 1.0 or less.

The object detection device 1 sets the score to 0.0 when the overlapratio is smaller than a first predetermined value 2201 (for example,0.2). The object detection device 1 sets the score to 1.0 when theoverlap ratio is a second predetermined value 2202 (for example, 0.8) ormore. When the overlap ratio is equal to or larger than the firstpredetermined value 2201 (for example, 0.2) and smaller than the secondpredetermined value 2202 (for example, 0.8), the object detection device1 calculates a score according to the value of the overlap ratio.Specifically, the object detection device 1 calculates the score in thiscase using the following Expression (6).

Score=(value of overlap ratio−first predetermined value 2201)/(secondpredetermined value 2202−first predetermined value 2201)  (6)

That is, the object detection device 1 calculates the score in this caseby proportional calculation of the difference between the value of theoverlap ratio and the first predetermined value 2201 and the differencebetween the second predetermined value 2202 and the first predeterminedvalue 2201. The score varies between 0.0 and 1.0.

In the examples of FIGS. 21(a) and 21(b), there are four point cloudintervals of 0.3 m between both ends of the width that is one of theparameters of the detection object. As illustrated in FIG. 21(a), theobject detection device 1 estimates the lower limit value in the rangeof the estimated value of the width as 0.3 m×4=1.2 m. As illustrated inFIG. 21(b), the object detection device 1 estimates the upper limitvalue in the range of the estimated value of the width as 0.3m×(4+2)=1.8 m. That is, in the examples of FIGS. 21(a) and 21(b), theobject detection device 1 calculates the range of the estimated value ofthe width as a range of 1.2 m or more and less than 1.8 m. In FIG. 22 ,a range in which the score of the width in the type of four-wheeledvehicle is 1.0 is illustrated. The range in which the score of the widthin the type of four-wheeled vehicle is 1.0 is 1.5 m or more and 2.0 m orless when referring to the range of the second threshold 1802 or moreand the third threshold 1803 or less illustrated in FIG. 18 . In theexample of FIG. 22 , the range of the union is a range of 1.2 m or moreand 2.0 m or less. The range of the intersection set is a range of 1.5 mor more and less than 1.8 m. Therefore, the overlap ratio is calculatedas (1.8 m−1.5 m)/(2.0 m−1.2 m)=0.375 using the above Expression (5). Theoverlap ratio of 0.375 is greater than or equal to the firstpredetermined value 2201 (0.2) and smaller than the second predeterminedvalue 2202 (0.8). Therefore, the score is calculated as(0.375−0.2)/(0.8−0.2)=0.29 using the above Expression (6). In theexamples of FIGS. 21(a) and 21 (b), the object detection device 1evaluates that the width of the detection object does not satisfy atleast the condition of the width of the four-wheeled vehicle.

The object detection device 1 similarly calculates the overlap ratio andthe score for the other types. The object detection device 1 calculatesthe overlap ratio and the score for the depth and the height, which areother parameters, similarly to the width parameter. Then, the objectdetection device 1 calculates the reliability for each type bymultiplying each score of the depth, the width, and the height, whichare parameters of the detection object, for each type. As a result, ifthe type of the detection object cannot be determined, the objectdetection device 1 performs re-scanning in the narrowing mode.

FIG. 23(a) is a diagram for explaining a lower limit value in the rangeof the estimated value of the parameter of the detection object in acase where the object illustrated in FIG. 21(a) is re-scanned in thenarrowing mode. FIG. 23(b) is a diagram for explaining an upper limitvalue in the range of the estimated value of the parameter of thedetection object in the case illustrated in FIG. 23(a). FIG. 23(c) is adiagram for explaining a case where the object illustrated in FIG. 21(c)is re-scanned in the narrowing mode and the object detection device 1detects the objects as separate objects.

FIGS. 23(a) and 23(b) illustrate a case where the object illustrated inFIGS. 21(a) and 21(b) is re-scanned in the narrowing mode in which thepoint cloud interval is 0.2 m. FIG. 23(c) illustrates a case where theobject illustrated in FIG. 21(c) is re-scanned in the narrowing mode inwhich the point cloud interval is 0.2 m.

In the examples of FIGS. 23(a) and 23(b), there are eight point cloudintervals of 0.2 m between both ends of the width that is one of theparameters of the detection object. As illustrated in FIG. 23(a), theobject detection device 1 estimates the lower limit value in the rangeof the estimated value of the width as 0.2 m×8=1.6 m. As illustrated inFIG. 23(b), the object detection device 1 estimates the upper limitvalue in the range of the estimated value of the width as 0.2m×(8+2)=2.0 m. That is, in the examples of FIGS. 23(a) and 23(b), theobject detection device 1 calculates the range of the estimated value ofthe width as a range of 1.6 m or more and less than 2.0 m. The range inwhich the score of the width in the type of four-wheeled vehicle is 1.0is 1.5 m or more and 2.0 m or less. In the examples of FIGS. 23(a) and23(b), the range of the union is a range of 1.5 m or more and 2.0 m orless. The range of the intersection set is a range of 1.6 m or more andless than 2.0 m. Therefore, the overlap ratio is calculated as(2.0−1.6)/(2.0−1.5)=0.8 using the above Expression (5). The overlapratio of 0.8 is the second predetermined value 2202 (0.8) or more. Theobject detection device 1 has a score of 1.0. In the examples of FIGS.23(a) and 23(b), the object detection device 1 evaluates that the widthof the detection object satisfies at least the condition of the width ofthe four-wheeled vehicle.

As illustrated in FIG. 21(c), when the interval (0.25 m) between the twoobjects is shorter than the point cloud interval (0.3 m), the objectdetection device 1 detects the two objects as one detection object. Asillustrated in FIG. 23(c), when the interval (0.25 m) between the twoobjects is longer than the point cloud interval (0.2 m), the objectdetection device 1 can detect the two objects as separate detectionobjects.

As described above, the object detection device 1 can calculate thescore quantitatively evaluating the reliability serving as the basis fordetermining whether to perform the scanning in the narrowing mode usingthe overlap ratio. Therefore, since the object detection device 1 canmore accurately evaluate the reliability than the method illustrated inFIGS. 18 to 20 , the efficiency can be further improved while securingthe detection accuracy.

Embodiment 2

An object detection device 1 according to Embodiment 2 will be describedwith reference to FIGS. 24 and 25 . In the description of Embodiment 2,the description of the same configuration and operation as those ofEmbodiment 1 will be omitted.

FIG. 24 is a diagram illustrating an example of a detection object inthe object detection device 1 of Embodiment 2.

In Embodiment 1, as illustrated in FIG. 19 , the case where the objectdetection device 1 detects the preceding vehicle 1901 from behind hasbeen described. In Embodiment 2, a case where the object detectiondevice 1 detects a vehicle 2401 from the side like the vehicle 2401entering from the lateral direction at the intersection will bedescribed.

In the example of FIG. 24 , it is assumed that the value of the width,which is one of the parameters of the vehicle 2401 as the detectionobject, is 1.6 m, the value of the depth, which is one of theparameters, is 3.2 m, and the value of the height, which is one of theparameters, is 1.6 m. At this time, when the object detection device 1calculates the scores of the depth, the width, and the height assumingthat the vehicle 2401 is the preceding vehicle, the calculation resultof the score and the reliability and the determination result of thetype are as illustrated in FIG. 24 . That is, when the vehicle 2401 isregarded as a preceding vehicle, the score and the reliability are lowfor all types, and all types are determined as negative, so that theobject detection device 1 cannot determine the type of the detectionobject.

In the object detection device 1 of Embodiment 2, in a case where thereliability is low such as a case where the type of the detection objectcannot be determined, the reliability determination unit 205recalculates each score by replacing the depth and the width, which areparameters of the detection object, before registering the detectionobject in the caution-needed object list. The reliability determinationunit 205 of Embodiment 2 recalculates the reliability using therecalculated score. In the example of FIG. 24 , when the depth and thewidth are interchanged, the value of each parameter of the detectionobject becomes the same as that in FIG. 19 , so that the type of thedetection object can be determined as a four-wheeled vehicle. When it isdetermined that the recalculated reliability is low and the type of thedetection object is unknown, the reliability determination unit 205 ofEmbodiment 2 registers the detection object in the caution-needed objectlist as in Embodiment 1.

FIG. 25 is a flowchart illustrating a flow of processing related tocalculation of reliability performed by the object detection device 1 ofEmbodiment 2. The process illustrated in FIG. 25 corresponds to theprocessing illustrated in FIG. 17 in Embodiment 1.

The object detection device 1 of Embodiment 2 performs processingsimilar to that of Loop L171 illustrated in FIG. 17 . After performingLoop L171 by a predetermined number of types, the object detectiondevice 1 exits Loop L171 and proceeds to Step S2501.

In Step S2501, the object detection device 1 determines whether there isa type that can be determined as the type of the detection objectrepresented by the group. In a case where there is a type that can bedetermined, the object detection device 1 ends the processingillustrated in FIG. 25 . When there is no type that can be determined,the object detection device 1 proceeds to Loop L251. In Loop L251, theobject detection device 1 performs Steps S2502 to S2504 by the samenumber of types as Loop L171.

In Step S2502, the object detection device 1 replaces the depth and thewidth of the detection object represented by the group and recalculatesthe respective scores.

In Step S2503, the object detection device 1 replaces the depth and thewidth and multiplies them by the recalculated score.

In Step S2504, the object detection device 1 replaces the calculationresult of Step S2503 with the reliability.

After Step S2504, the object detection device 1 performs Loop L251 foranother type. After performing Loop L251 by the number of types, theobject detection device 1 exits Loop L251 and ends the processingillustrated in FIG. 25.

As described above, in the object detection device 1 according toEmbodiment 2, in a case where the reliability is low, the reliabilitydetermination unit 205 recalculates each score by replacing the depthand the width, which are parameters of the detection object, beforeregistering the detection object in the caution-needed object list. Thereliability determination unit 205 of Embodiment 2 recalculates thereliability using the recalculated score, and registers the detectionobject in the caution-needed object list when the recalculatedreliability is low.

As a result, the object detection device 1 of Embodiment 2 canappropriately determine the type even when a vehicle is detected fromthe side, such as a vehicle entering from the lateral direction at theintersection. Therefore, the object detection device 1 of Embodiment 2can detect an object with high accuracy flexibly corresponding tovarious surrounding situations, and can improve the detection accuracyas compared with Embodiment 1.

Embodiment 3

An object detection device 1 according to Embodiment 3 will be describedwith reference to FIG. 26 . In the description of Embodiment 3, thedescription of the same configuration and operation as those ofEmbodiment 1 will be omitted.

In Embodiment 1, when it is determined that the type of the detectionobject is unknown, the detection object is registered in thecaution-needed object list. In Embodiment 3, attention is paid not onlyto the determination result of the type of the detection object but alsoto the speed of the detection object. Specifically, in the objectdetection device 1 of Embodiment 3, when the reliability is low and thetype of the detection object is determined to be unknown, thereliability determination unit 205 calculates the variance value of thevelocity of the detection object before registering the detection objectin the caution-needed object list. In a case where the calculatedvariance value is larger than a threshold, the reliability determinationunit 205 of Embodiment 3 registers the detection object in thecaution-needed object list.

The reliability determination unit 205 of Embodiment 3 can calculate thespeed and the moving direction of the detection object by the objecttracking unit 207 that tracks the movement of the detection object. Theobject tracking unit 207 calculates the speed and the moving directionof the detection object by comparing the current position and theprevious position of the detection object. The object tracking unit 207can specify the previous position of the detection object by searchingfor the position where the detection object was detected in the previousprocessing around the current position of the detection object andextracting the optimum position.

FIG. 26 is a flowchart illustrating a flow of processing performed bythe object detection device 1 of Embodiment 3. The processingillustrated in FIG. 26 corresponds to Loop L71 illustrated in FIG. 7 inEmbodiment 1.

In Loop L71 illustrated in FIG. 26 , the object detection device 1 ofEmbodiment 3 performs Steps S704 to S706 similarly to Loop L71illustrated in FIG. 7 . However, the object detection device 1 ofEmbodiment 3 performs Steps S2601 to S2606 between Steps S705 and S706in Loop L71 illustrated in FIG. 26 . That is, when it is determined inStep S705 that the type of the detection object is unknown, the objectdetection device 1 of Embodiment 3 proceeds to Step S2601.

In Step S2601, the object detection device 1 specifies the previousposition of the detection object. As described above, the objectdetection device 1 specifies the previous position of the detectionobject by searching for the position where the detection object wasdetected in the previous processing around the current position of thedetection object.

In Step S2602, the object detection device 1 determines whether theprevious position of the detection object has been specified in StepS2601. When the previous position of the detection object is notspecified, the object detection device 1 performs Loop L71 on anotherdetection object. When the previous position of the detection object isspecified, the object detection device 1 proceeds to Step S2603.

In Step S2603, the object detection device 1 calculates the speed andthe moving direction of the detection object by comparing the currentposition and the previous position of the detection object.

In Step S2604, the object detection device 1 determines whether thenumber of times of detection of the detection object is larger than athreshold A. The threshold A is an upper limit value of the number oftimes of detection such that the calculation result when the variance ofthe speed is calculated lacks statistical reliability. When the numberof times of detection of the detection object is equal to or less thanthe threshold A, the object detection device 1 performs Loop L71 onanother detection object. When the number of times of detection of thedetection object is larger than the threshold A, the object detectiondevice 1 proceeds to Step S2605.

In Step S2605, the object detection device 1 calculates the variance ofthe velocity of the detection object.

In Step S2606, the object detection device 1 determines whether thecalculated variance value of the speed is larger than a threshold B. Thethreshold B is an upper limit value of a variance value at which it canbe determined that the detection object has speed stability. That is,when the speed of the detection object rapidly changes, the variancevalue of the speed of the detection object is larger than the thresholdB. When the variance value of the speed is equal to or less than thethreshold B, the object detection device 1 performs Loop L71 on anotherdetection object. In a case where the variance value of the speed islarger than the threshold B, the object detection device 1 proceeds toStep S706 and registers the detection object in the caution-needed list.

After Step S706, the object detection device 1 performs Loop L71 onanother detection object. After performing Loop L71 by the number ofdetection objects, the object detection device 1 exits Loop L71 and endsthe processing illustrated in FIG. 26 .

As described above, in the object detection device 1 of Embodiment 3,when the reliability is low and the type of the detection object isdetermined to be unknown, the reliability determination unit 205calculates the variance value of the speed of the detection objectbefore registering the detection object in the caution-needed objectlist. In a case where the calculated variance value is larger than athreshold, the reliability determination unit 205 of Embodiment 3registers the detection object in the caution-needed object list.

As a result, in the object detection device 1 of Embodiment 3, it ispossible to preferentially scan a detection object whose speed rapidlychanges in the narrowing mode among detection objects with lowreliability such as detection objects with an unknown type. Therefore,the object detection device 1 of Embodiment 3 can flexibly cope withvarious surrounding situations, and the efficiency can be improved ascompared with Embodiment 1.

In FIG. 26 , the object detection device 1 according to Embodiment 3performs Steps S2601 to S2605 between Steps S705 and S706. The objectdetection device 1 of Embodiment 3 is not limited thereto, and StepsS2601 to S2605 may be performed in parallel with Step S705.Specifically, when determining in Step S704 that the detection object islocated in the caution-needed area, the object detection device 1 ofEmbodiment 3 proceeds to each of Steps S705 and S2601. Then, in a casewhere it is determined in Step S705 that the type of the detectionobject is unknown, the object detection device 1 of Embodiment 3proceeds to Step S706, and in a case where the variance value of thespeed is larger than the threshold B in Step S2606, the object detectiondevice 1 proceeds to Step S706. As a result, when it is determined thatthe type of the detection object is unknown or when the variance valueof the speed of the detection object is larger than the threshold B, theobject detection device 1 of Embodiment 3 can scan the detection objectin the narrowing mode.

Note that, in the above embodiment, an example has been described inwhich the object detection device 1 includes the LiDAR 101 that emits alaser beam as a sensor that scans the surroundings of the vehicle. Theobject detection device 1 is not limited thereto, and may include asensor that emits an electromagnetic wave or a sound wave as a sensorthat scans the periphery of the vehicle.

Others

Further, the present invention is not limited to the above embodiments,and various modifications may be contained. For example, theabove-described embodiments of the present invention have been describedin detail in a clearly understandable way, and are not necessarilylimited to those having all the described configurations. In addition,some of the configurations of a certain embodiment may be replaced withthe configurations of the other embodiments, and the configurations ofthe other embodiments may be added to the configurations of the subjectembodiment. In addition, some of the configurations of each embodimentmay be omitted, replaced with other configurations, and added to otherconfigurations.

Each of the above configurations, functions, processing units,processing means, and the like may be partially or entirely achieved byhardware by, for example, designing by an integrated circuit. Inaddition, the configurations and the functions may be realized insoftware such that a processor analyzes and performs a program whichrealizes each function. Information such as a program, a tape, and afile for realizing each function can be stored in a recording devicesuch as a memory, a hard disk, and a solid state drive (SSD), or arecording medium such as an IC card, an SD card, and a DVD.

In addition, only control lines and information lines considered to benecessary for explanation are illustrated, but not all the control linesand the information lines for a product are illustrated. In practice,almost all the configurations may be considered to be connected to eachother.

REFERENCE SIGNS LIST

-   1 object detection device-   101 LiDAR (sensor)-   201 point cloud acquisition unit-   204 object detection unit-   205 reliability determination unit-   503 caution-needed object list

1. An object detection device comprising: a point cloud acquisition unitconfigured to acquire point cloud data of an object existing in aperiphery of a vehicle according to a scanning result of a sensor thatscans the periphery of the vehicle; an object detection unit configuredto detect the object based on the point cloud data; and a reliabilitydetermination unit configured to determine a reliability in a detectionresult of the object detection unit, wherein the point cloud acquisitionunit controls a scanning range and an irradiation density of the sensorbased on the reliability.
 2. The object detection device according toclaim 1, wherein the sensor can perform scanning in a normal mode and anarrowing mode in which the scanning range is reduced and theirradiation density is increased as compared with the normal mode, thereliability determination unit is configured to: determine a type of adetection object that is the object of the detection result when thereliability is high, when the reliability is low, the detection objectis registered in a caution-needed object list, the point cloudacquisition unit causes the sensor to re-scan the detection objectregistered in the caution-needed object list in the narrowing mode andre-acquires the point cloud data, the object detection unit re-detectsthe detection object based on the re-acquired point cloud data, and thereliability determination unit determines the reliability in are-detection result of the object detection unit.
 3. The objectdetection device according to claim 2, wherein the reliabilitydetermination unit excludes the detection object from the caution-neededobject list when the reliability in the re-detection result is high, andthe point cloud acquisition unit causes the sensor to scan a new objectdifferent from the detection object in the normal mode, and newlyacquires the point cloud data.
 4. The object detection device accordingto claim 2, wherein a parameter that is an index representing anattribute of the object and a possible range of the parameter for eachtype are determined in advance in the reliability determination unit,and the reliability determination unit calculates a score obtained byquantifying compatibility of the parameter of the detection object withthe range, and calculates the reliability using the score.
 5. The objectdetection device according to claim 4, wherein the parameter is each ofa depth, a width, and a height of the object, and the reliabilitydetermination unit is configured to: when the reliability is low,replace the depth and the width of the detection object before thedetection object is registered in the caution-needed object list torecalculate the score, and recalculate the reliability using therecalculated score; and register the detection object in thecaution-needed object list when the recalculated reliability is low. 6.The object detection device according to claim 2, wherein thereliability determination unit calculates a variance of a speed of thedetection object before the detection object is registered in thecaution-needed object list when the reliability degree is low, andregisters the detection object in the caution-needed object list when avariance value is larger than a threshold.